Reservoir simulation requires a three dimensional model of the reservoir. The model employs a grid or some other technique to divide the reservoir region into cells, each cell having an associated value for each of one or more geophysical properties. Typical properties include porosity, permeability, and water saturation. Millions of cells may be needed to represent the spatial distribution of properties adequately enough for a flow simulator to predict oil and gas recovery, production profiles and to assist in planning the number of wells. As the number of cells increases, the geocellular model becomes more burdensome to dynamically simulate.
One approach to limiting the size of the model is to model only the cells in the regions of interest. However, to be able to cut away or ignore the portions of the subsurface that do not affect the reservoir behavior, the analyst must be able to identify those regions of particular interest. Generally, such identification is accomplished via manipulation of a three dimensional seismic image that is controlled by the analyst. For example, the analyst may ask that certain attributes of the seismic waves be calculated and displayed, e.g., amplitude, phase, correlation, and associated derived properties. The analyst may assign color and opacity values to different ranges of attribute values to highlight certain portions of the data. The analyst may select different viewpoints and “slice” orientations through the data volume. The analyst may “flatten” the image along a selected reference horizon, or flatten all of the horizons. In short, the analyst has an arsenal of tools at his disposal for perusing the seismic image to identify particular regions of interest. However, the time required for the analyst to identify regions of interest is often quite limited, and in practice it is common for decisions to be made with analysis completed on only a small fraction of the seismic image data.